Memory-based evolutionary game on small-world network with tunable heterogeneity

Xiao-Heng Deng, Yi Liu and Zhi-Gang Chen

Physica A: Statistical Mechanics and its Applications, 2010, vol. 389, issue 22, 5173-5181

Abstract: Most papers about evolutionary games on graph assume agents have no memory. Yet, in the real world, interaction history can also affect an agent’s decision. So we introduce a memory-based agent model and investigate the Prisoner’s Dilemma game on a Heterogeneous Newman–Watts small-world network based on a Genetic Algorithm, focusing on heterogeneity’s role in the emergence of cooperative behaviors. In contrast with previous results, we find that a different heterogeneity parameter domain range imposes an entirely different impact on the cooperation fraction. In the parameter range corresponding to networks with extremely high heterogeneity, the decrease in heterogeneity greatly promotes the proportion of cooperation strategy, while in the remaining parameter range, which relates to relatively homogeneous networks, the variation of heterogeneity barely affects the cooperation fraction. Also our study provides a detailed insight into the microscopic factors that contribute to the performance of cooperation frequency.

Keywords: Complex networks; Prisoner’s Dilemma game; Cooperative behaviors; Heterogeneity (search for similar items in EconPapers)
Date: 2010
References: View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:389:y:2010:i:22:p:5173-5181

DOI: 10.1016/j.physa.2010.08.004

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